Understanding how outdoor environments affect mental health outcomes is vital in today’s fast-paced and urbanized society. Recently, advancements in data-gathering technologies and deep learning have facilitated the study of the relationship between the outdoor environment and human perception. In a systematic review, we investigate how deep learning techniques can shed light on a better understanding of the influence of outdoor environments on human perceptions and emotions, with an emphasis on mental health outcomes. We have systematically reviewed 40 articles published in SCOPUS and the Web of Science databases which were the published papers between 2016 and 2023. The study presents and utilizes a novel topic modeling method to identify coherent keywords. By extracting the top words of each research topic, and identifying the current topics, we indicate that current studies are classified into three areas. The first topic was “Urban Perception and Environmental Factors” where the studies aimed to evaluate perceptions and mental health outcomes. Within this topic, the studies were divided based on human emotions, mood, stress, and urban features impacts. The second topic was titled “Data Analysis and Urban Imagery in Modeling” which focused on refining deep learning techniques, data collection methods, and participants’ variability to understand human perceptions more accurately. The last topic was named “Greenery and visual exposure in urban spaces” which focused on the impact of the amount and the exposure of green features on mental health and perceptions. Upon reviewing the papers, this study provides a guide for subsequent research to enhance the view of using deep learning techniques to understand how urban environments influence mental health. It also provides various suggestions that should be taken into account when planning outdoor spaces.

在当今快节奏的城市化社会中，了解户外环境如何影响心理健康结果至关重要。最近，数据收集技术和深度学习的进步促进了户外环境与人类感知之间关系的研究。在系统综述中，我们研究了深度学习技术如何更好地理解户外环境对人类感知和情绪的影响，重点是心理健康结果。我们系统地回顾了 SCOPUS 和 Web of Science 数据库中发表的 40 篇文章，这些文章是 2016 年至 2023 年间发表的论文。该研究提出并利用一种新颖的主题建模方法来识别连贯的关键词。通过提取每个研究主题的热门词并确定当前主题，我们表明当前研究分为三个领域。第一个主题是“城市认知和环境因素”，研究旨在评估认知和心理健康结果。在这个主题中，研究根据人类情绪、情绪、压力和城市特征影响进行划分。第二个主题为“建模中的数据分析和城市意象”，重点是完善深度学习技术、数据收集方法和参与者的可变性，以更准确地理解人类的感知。最后一个主题名为“城市空间中的绿化和视觉暴露”，重点关注绿色特征的数量和暴露对心理健康和感知的影响。通过回顾这些论文，本研究为后续研究提供了指导，以增强使用深度学习技术来理解城市环境如何影响心理健康的观点。它还提供了规划室外空间时应考虑的各种建议。